KReator aims at developing a common methodology for learning, modelling and inference in a relational probabilistic framework. Currently, the development of KReator is still in a very early stage but already supports Bayesian logic programs, Markov logic networks, and in particular a new approach for using maximum entropy methods in a relational context. KReator aims at providing a common interface for different approaches to statistical relational learning. That way, KReator supports the researcher and knowledge engineer in de-
veloping knowledge bases and employing them in a common and easy-to-use fashion. KReator provides abstract interfaces to common structures like knowledge bases and queries and different approaches can be integrated easily by implementing these interfaces. New approaches can also make use of the logical structures (for predicates, constants, atoms, etc.) already implemented in KReator. As KReator provides a common methodology to address the functionalities of an approach--in particular KReator provides project management, file handling, and a graphical and a console-based user interface--developers only need to take care of
the essential components of their approach and can neglect the tedious tasks of implementing the surrounding infrastructure. This also yields a great advantage for the user of the system as different approaches can be employed using the same interface and the same methods.